GATES: Cost-aware Dynamic Workflow Scheduling via Graph Attention Networks and Evolution Strategy

TL;DR

GATES introduces a novel deep reinforcement learning approach combining Graph Attention Networks and Evolution Strategy to improve cost-aware dynamic workflow scheduling in cloud computing, effectively handling task dependencies and resource variability.

Contribution

It proposes a new DRL method, GATES, that captures task relationships and VM importance, enhancing scheduling stability and performance in dynamic workflows.

Findings

GATES outperforms several state-of-the-art algorithms in experiments.

It effectively models task dependencies using Graph Attention Networks.

GATES demonstrates stable learning with Evolution Strategy.

Abstract

Cost-aware Dynamic Workflow Scheduling (CADWS) is a key challenge in cloud computing, focusing on devising an effective scheduling policy to efficiently schedule dynamically arriving workflow tasks, represented as Directed Acyclic Graphs (DAG), to suitable virtual machines (VMs). Deep reinforcement learning (DRL) has been widely employed for automated scheduling policy design. However, the performance of DRL is heavily influenced by the design of the problem-tailored policy network and is highly sensitive to hyperparameters and the design of reward feedback. Considering the above-mentioned issues, this study proposes a novel DRL method combining Graph Attention Networks-based policy network and Evolution Strategy, referred to as GATES. The contributions of GATES are summarized as follows: (1) GATES can capture the impact of current task scheduling on subsequent tasks by learning the topological relationships between tasks in a DAG. (2) GATES can assess the importance of each VM to the ready task, enabling it to adapt to dynamically changing VM resources. (3) Utilizing Evolution Strategy's robustness, exploratory nature, and tolerance for delayed rewards, GATES achieves stable policy learning in CADWS. Extensive experimental results demonstrate the superiority of the proposed GATES in CADWS, outperforming several state-of-the-art algorithms. The source code is available at: https://github.com/YaShen998/GATES.